Immune Network Algorithm

Immune Network Algorithm #

Name #

Immune Network Algorithm (INA), also known as:

  • Artificial Immune Network (AIN)
  • Immune Network Model (INM)
  • Idiotypic Network Algorithm

Taxonomy #

The Immune Network Algorithm is a biologically inspired optimization algorithm that falls under the broader category of Artificial Immune Systems (AIS) in the field of Computational Intelligence.

  • Computational Intelligence
    • Biologically Inspired Computation
      • Artificial Immune Systems (AIS)
        • Immune Network Algorithm (INA)

Strategy #

The Immune Network Algorithm is inspired by the idiotypic network theory proposed by Niels Jerne, which suggests that antibodies in the immune system not only recognize and bind to antigens but also interact with each other to form a network of stimulation and suppression. In the algorithm, each antibody represents a candidate solution to the optimization problem, and the affinity between antibodies represents their similarity.

The algorithm maintains a population of antibodies that evolve over time through a process of cloning, mutation, and selection. Antibodies with higher affinities to the antigen (better solutions) are more likely to be selected for cloning and mutation, while those with lower affinities are suppressed and may be eliminated from the population.

The interactions between antibodies in the network help maintain diversity in the population and prevent premature convergence to suboptimal solutions. The stimulation and suppression effects also allow the algorithm to balance exploration and exploitation during the search process.

Procedure #

Data Structures:

  • Antibody: Represents a candidate solution to the optimization problem.
  • Affinity Matrix: Stores the affinity (similarity) between each pair of antibodies in the population.


  • Population Size: The number of antibodies in the population.
  • Cloning Rate: The proportion of antibodies selected for cloning in each iteration.
  • Mutation Rate: The probability of each element in an antibody being mutated.
  • Suppression Threshold: The affinity threshold below which an antibody is suppressed.
  • Termination Criterion: The condition for stopping the algorithm, such as a maximum number of iterations or a target fitness value.


  1. Initialize the population of antibodies randomly.
  2. Evaluate the affinity of each antibody to the antigen (fitness function).
  3. Calculate the affinity between each pair of antibodies and store in the Affinity Matrix.
  4. While the termination criterion is not met, do:
    1. Select a subset of antibodies with the highest affinities for cloning proportional to their affinity (Clonal Selection).
    2. Clone the selected antibodies.
    3. Mutate the cloned antibodies with a probability based on the Mutation Rate (Somatic Hypermutation).
    4. Evaluate the affinity of the mutated clones to the antigen.
    5. Update the Affinity Matrix with the affinities between the mutated clones and the existing antibodies.
    6. Suppress antibodies with affinities below the Suppression Threshold.
    7. Replace the suppressed antibodies with new randomly generated ones.
  5. Return the antibody with the highest affinity as the best solution found.

Considerations #


  • Maintains diversity in the population, reducing the risk of premature convergence.
  • Balances exploration and exploitation through the stimulation and suppression mechanisms.
  • Can adapt to dynamic environments where the optimal solution may change over time.


  • The computation of the Affinity Matrix can be computationally expensive, especially for large population sizes.
  • The performance of the algorithm is sensitive to the choice of parameters, such as the Suppression Threshold and Mutation Rate.
  • May require a large number of iterations to converge to the optimal solution, particularly for high-dimensional problems.

Heuristics #

Population Size:

  • Start with a population size that is large enough to maintain diversity but not so large that it becomes computationally prohibitive.
  • Consider increasing the population size for more complex problems or those with many local optima.

Cloning Rate:

  • A higher cloning rate can lead to faster convergence but may reduce diversity in the population.
  • A lower cloning rate can maintain diversity but may slow down convergence.
  • Experiment with different cloning rates to find a balance suitable for the problem at hand.

Mutation Rate:

  • A higher mutation rate can increase exploration but may disrupt good solutions.
  • A lower mutation rate can help preserve good solutions but may limit exploration.
  • Consider using an adaptive mutation rate that decreases over time as the population converges.

Suppression Threshold:

  • A higher suppression threshold can maintain more diversity but may slow down convergence.
  • A lower suppression threshold can speed up convergence but may lead to premature convergence to suboptimal solutions.
  • Adjust the suppression threshold based on the desired balance between diversity and convergence speed.

Termination Criterion:

  • Use a maximum number of iterations as a safeguard to prevent the algorithm from running indefinitely.
  • Consider adding a target fitness value as a termination criterion if the optimal solution’s fitness is known or can be estimated.
  • Monitor the progress of the algorithm and stop if the best solution does not improve for a specified number of iterations.